Functional and structural characterization of a novel isoamylase from ostreococcus tauri and role of the n-terminal domain

Background: The debranching starch enzymes, isoamylase 1 and 2 are well-conserved enzymes present in almost all the photosynthetic organisms. These enzymes are involved in the crystallization process of starch and are key components which remove misplaced α-1,6 ramifications on the final molecule. Aim: In this work, we performed a functional and structural study of a novel isoamylase from Ostreococcus tauri. Methods: We identified conserved amino acid residues possibly involved in catalysis. We also identified a region at the N-terminal end that resembles a Carbohydrate Binding Domain (CBM), which is more related to the family CBM48, but has no spatial conservation of the residues involved in carbohydrate binding. Results: The cloning, expression and biochemical characterization of this N-terminal region confirmed that it binds to polysaccharides, showing greater capacity for binding to amylopectin rather than total starch or amylose. Conclusion: This module could be a variant of the CBM48 family or it could be classified within a new CBM family.Fil: Hedin, Nicolas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; ArgentinaFil: Barchiesi, Julieta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; ArgentinaFil: Gomez Casati, Diego Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; ArgentinaFil: Busi, María Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; Argentin

botXminer: mining biomedical literature with a new web-based application

This paper outlines botXminer, a publicly available application to search XML-formatted MEDLINE(®) data in a complete, object-relational schema implemented in Oracle(®) XML DB. An advantage offered by botXminer is that it can generate quantitative results with certain queries that are not feasible through the Entrez-PubMed(®) interface. After retrieving citations associated with user-supplied search terms, MEDLINE fields (title, abstract, journal, MeSH(®) and chemical) and terms (MeSH qualifiers and descriptors, keywords, author, gene symbol and chemical), these citations are grouped and displayed as tabulated or graphic results. This work represents an extension of previous research for integrating these citations with relational systems. botXminer has a user-friendly, intuitive interface that can be freely accessed at

Tractography patterns of subthalamic nucleus deep brain stimulation

Deep brain stimulation therapy is an effective symptomatic treatment for Parkinson’s disease, yet the precise mechanisms responsible for its therapeutic effects remain unclear. Although the targets of deep brain stimulation are grey matter structures, axonal modulation is known to play an important role in deep brain stimulation’s therapeutic mechanism. Several white matter structures in proximity to the subthalamic nucleus have been implicated in the clinical benefits of deep brain stimulation for Parkinson’s disease. We assessed the connectivity patterns that characterize clinically beneficial electrodes in Parkinson’s disease patients, after deep brain stimulation of the subthalamic nucleus. We evaluated 22 patients with Parkinson’s disease (11 females, age 57 ± 9.1 years, disease duration 13.3 ± 6.3 years) who received bilateral deep brain stimulation of the subthalamic nucleus at the National Institutes of Health. During an initial electrode screening session, one month after deep brain stimulation implantation, the clinical benefits of each contact were determined. The electrode was localized by coregistering preoperative magnetic resonance imaging and postoperative computer tomography images and the volume of tissue activated was estimated from stimulation voltage and impedance. Brain connectivity for the volume of tissue activated of deep brain stimulation contacts was assessed using probabilistic tractography with diffusion-tensor data. Areas most frequently connected to clinically effective contacts included the thalamus, substantia nigra, brainstem and superior frontal gyrus. A series of discriminant analyses demonstrated that the strength of connectivity to the superior frontal gyrus and the thalamus were positively associated with clinical effectiveness. The connectivity patterns observed in our study suggest that the modulation of white matter tracts directed to the superior frontal gyrus and the thalamus is associated with favourable clinical outcomes and may contribute to the therapeutic effects of deep brain stimulation. Our method can be further developed to reliably identify effective deep brain stimulation contacts and aid in the programming process

Microcalcification Detection Applying Artificial Neural Networks and Mathematical Morphology in Digital Mammograms

Breast cancer is one of the leading causes to women mortality in the world and early detection is an important means to reduce the mortality rate. The presence of microcalcifications clusters has been considered as a very important indicator of malignant types of breast cancer and its detection is important to prevent and treat the disease. This paper presents an alternative and effective approach in order to detect microcalcifications clusters in digitized mammograms based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. A k-means algorithm is used to cluster the data based on the features vectors and finally an artificial neural network-based classifier is applied and the classification performance is evaluated by a ROC curve. Experimental results indicate that the percentage of correct classification was 99.72%, obtaining 100% true positive (sensitivity) and 99.67% false positive (specificity), with the best classifier proposed. In case of the best classifier, we obtained a performance evaluation of classification of Az = 0.987

Longitudinal MR spectroscopy of neurodegeneration in multiple sclerosis with diffusion of the intra-axonal constituent N-acetylaspartate

Neuro Imaging Researc

Social Media for the Promotion of Holistic Self-Participatory Care: An Evidence Based Approach

Objectives: As health information is becoming increasingly accessible, social media offers ample opportunities to track, be informed, share and promote health. These authors explore how social media and holistic care may work together; more specifically however, our objective is to document, from different perspectives, how social networks have impacted, supported and helped sustain holistic self-participatory care. Methods: A literature review was performed to investigate the use of social media for promoting health in general and complementary alternative care in particular. We also explore a case study of an intervention for improving the health of Greek senior citizens through digital and other means. Results: The Health Belief Model provides a framework for assessing the benefits of social media interventions in promoting comprehensive participatory self-care. Some interventions are particularly effective when integrating social media with real-world encounters. Yet not all social media tools are evidence-based and efficacious. Interestingly, social media is also used to elicit patient ratings of treatments (e.g., for depression), often demonstrating the effectiveness of complementary treatments, such as yoga and mindfulness meditation. Conclusions: To facilitate the use of social media for the promotion of complementary alternative medicine through self-quantification, social connectedness and sharing of experiences, exploration of concrete and abstract ideas are presented herewithin. The main mechanisms by which social support may help improve health - emotional support, an ability to share experiences, and non-hierarchal roles, emphasizing reciprocity in giving and receiving support – are integral to social media and provide great hope for its effective us

Techniques of FECG signal analysis: detection and processing for fetal monitoring

Fetal heart rate monitoring is a technique for obtaining important information about the condition of a fetus during pregnancy and labor, by detecting the FECG signal generated by the heart of the fetus. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature. A comparative study has been carried out to show the performance of various methods. This paper opens up a passage to biomedical researchers, physicians and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system by providing valuable information to help them in developing more dominant, flexible and resourceful application

Stereoscopic three-dimensional visualization applied to multimodal brain images: Clinical applications and a functional connectivity atlas

Effective visualization is central to the exploration and comprehension of brain imaging data. While MRI data are acquired in three-dimensional space, the methods for visualizing such data have rarely taken advantage of three-dimensional stereoscopic technologies. We present here results of stereoscopic visualization of clinical data, as well as an atlas of whole-brain functional connectivity. In comparison with traditional 3D rendering techniques, we demonstrate the utility of stereoscopic visualizations to provide an intuitive description of the exact location and the relative sizes of various brain landmarks, structures and lesions. In the case of resting state fMRI, stereoscopic 3D visualization facilitated comprehension of the anatomical position of complex large-scale functional connectivity patterns. Overall, stereoscopic visualization improves the intuitive visual comprehension of image contents, and brings increased dimensionality to visualization of traditional MRI data, as well as patterns of functional connectivity

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI☆

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images